esmap ifc re csp training world bank romero

8/21/2019 ESMAP IFC RE CSP Training World Bank Romero

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CSP: Solar Resource Assessment


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Agenda

2

THE POWER OF THE SUN

Solar Datasets

The need for ground measurements

Output quality: CSP vs. PV


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The power of the sun

3

In less than one hour the sun delivers more

energy to the Earth’s surface than the wholeworld is consuming within a year.

Source Image: http://nineplanets.org/sol.html http://www.youtube.com/watch?v=OIP9FTWEpy4


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Solar Radiation

4

• Direct solar radiation is the radiation that

comes directly from the sun, with minimal

attenuation by the Earth’s atmosphere or

obstacles.

• Diffuse solar radiation is that which is

scattered, absorbed, and reflected within

the atmosphere, mostly by clouds, but also

by particulate matter and gas molecules.

•The direct and diffuse components together

are referred to as total or global radiation.

*Source:

mppoweruk.com

Direct

radiationScattered

radiation

Reflected

radiation

*Source :SECO


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Solar Data

Global Horizontal (GHI) = Direct Normal (DNI) x cos(θ) + Diffuse Horizontal (DHI)

• Global horizontal insolation (GHI):Solar radiation measured with an

instrument mounted horizontally, so

that it sees the whole sky (direct plus

diffuse).(Pyranometer)

• Diffuse horizontal insolation (DHI):

Measured using an instrument that

has a shade to block out the direct

radiation.

• Direct normal insolation (DNI) is

measured using an instrument that

tracks the sun and shades out the

diffuse, it only records the direct

component.(Pyrheliometer)


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Solar Resources

6

Flat-plate photovoltaic

devices utilize both diffuseand direct radiation. The

pertinent radiation is the

global horizontal insolation

(GHI).

Mirrors and other

concentrating optics is only

able to effectively focus the

direct component, so “direct

normal” solar radiation (DNI)

is most relevant to these

collectors.

Commonly, solar equipment

is tilted relative to

horizontal. DNI and GHI data

can be used to estimate or

model the solar radiation in

the plane of interest, global

tilt insolation (GTI).

*Source SECO Fixed horizontal

collecting surfaceFixed tilted

collecting surfaceTracking collecting

surface

Direct

irradiance

Direct

irradianceDirect

irradiance

Global tilt

irradianceGlobal horizontal

irradiance

Direct normal irradiance

Diffuseirradiance

Diffuse

irradiance

Selection of proper DNI sites is critical to Dish Projects Successful output


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7

Global Annual Solar Radiation (KWh/sq m.y)


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Solar resource for CSP technologies (DNI in Kwh/m2/y)

Source: (IEA, 2010) from Breyer & Kenies, 2009 based on DNI data from DLR-ISIS (Lohman, et al. 2006)

• Most favourable areas for CSP are: North Africa, southern Africa, the Middle-

East, southern Europe, north western India, the south western United States,Mexico, Peru, Chile, the western part of China and Australia.

•The IEA estimates CSP could provide up to 11.3% global electricity by 2050.


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What is good DNI?

Financial viability of projects will depend upon the resource, technologyand project costs, and the extent of government driven financial support.

Current costs of the technology and constraints on financial support

indicate that only projects that are located in the areas with the highest

direct normal irradiation are likely to be viable in the near future withannual average direct normal irradiation values of greater than 2.2

MWh/m2/year or 6.0 kWh/m2/day.


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Solar Datasets

Several satellite-derived DNI datasets with international coverage are available, but their properties

(input data source, data generation method, grid resolution, spatial and time resolution, uncertainty,

etc.), are not always well known or understood by the stakeholders involved in the planning process.

Public datasets: SSE v6 (NASA), CSR (NREL), SUNY (NREL) or Satel-Light (ENTPE). They cover several

countries, growing in extension each year.

Partially public datasets: SoDa/HelioClim (Ecole de Mines) and DLR-Solemi

Commercial datasets: Meteonorm, Focus Solar, solargis (GeoModel), EnMetSol, Ir-SOLaV, s2m or

3TIER. These datasets have mostly global coverage, and are reportedly based on better radiative

models and input data. It has to be shown whether this translates into higher-accuracy DNI results.


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Solar Resources – TMY3 and others

NREL's TMY3 Data

•TMY3 dataset is made up of historical observations from a 10-30 year history that

are selected as representative of the location and concatenated into a typical

meteorological year.[NSRDB Database]

• This data can be very good for monthly averages but is terrible for hourly and

daily data. NREL says "The TMY should not be used to predict weather for a particular period of time, nor is it an appropriate basis for evaluating real-time

energy production or efficiencies for building design applications or solar

conversion systems." (TMY3 User Manual).

http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.htmlhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html


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Solar Resources – TMY3 and others

NREL's TMY3 Data

•TMY3 dataset is made up of historical observations from a 10-30 year history that are

selected as representative of the location and concatenated into a typical meteorological

year.[NSRDB Database]• This data can be very good for monthly averages but is terrible for hourly and daily

data. NREL says "The TMY should not be used to predict weather for a particular period

of time, nor is it an appropriate basis for evaluating real-time energy production or

efficiencies for building design applications or solar conversion systems." (TMY3 User

Manual).

Class of NSRDB

DataUncertainty Remarks

ILowest uncertainty

data

Less then 25% of the data for the 15-year period of record exceeds an

uncertainty of 11%

IIHigher uncertainty

data

Greater then 25% of the data for the 15-year period of record exceeds

an uncertainty of 11%

IIIIncomplete period of

record.Algorithm used to complete data set

Solar Resource

TMY3

Actual Data

http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.htmlhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://www.nrel.gov/docs/fy08osti/43156.pdfhttp://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html


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Measuring DNI: Pyrheliometers

•5.7º Field of View

•Mounted in Solar Tracker

•Broadband Response

•0.3 - 3.0 mm (Quartz

window)

•Responsivity: 8-10 mV/Wm-

2

•Around 2% uncertainty

Source: The Eppley Laboratory, Inc.


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The need for ground MEASUREMENTS•Datasets are considered adequate for planning purposes, but planners

should be aware of the uncertainty associated to the data. Annual DNI sums

and yearly distribution differ very much among datasets for the same

specific sites since the models apply different atmospheric corrections.

•Locations with similar average DNIs can see variations of up to ± 9% in

annual electricity production due to differences in DNI frequency distribution.

(IEA, 2010).

•Ambient temperature, wind speed and direction and relative humidityconditions at the site AFFECT the performance.

•Therefore, satellite based datasets must be scaled with ground

measurements in order to obtain reliable and “bankable resource

assessments” during the project development phase.

•Solar resource uncertainty risk is perceived as one of the highest by

financiers. A minimum of one year of on-site measurements is required.

•The information obtained, together with satellite and historic data, must be

analyzed to produce long term estimates of the solar resource.


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Measurement Instruments

SES Power Curve Study July 2010| August 8, 2010 | 16

DNI meters:

• Thermopile sensor output voltage change proportional to DNI at 20C, but needs

temperature correction at other ambient temperatures

DR01

First Class Pyrheliometer

Temperature range: -40 to +80° CTemperature dependence: < ± 0.1 %/°C

Non stability (drift): < ± 1% per year

Calibration traceability: WRR

MSP DNI meters do not have temperature compensated outputs.Manufacturer advises the following temperature correction:

DNI corrected= DNI*(1+0.8%*(20-T(ºC))


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Impact from using temperature corrected DNI vs. raw DNI data

°

Before DNI correction

º

DNI

Power

DNI’

After DNI

correction

measured

power

estimated

power

Impact of DNI correction at high T

RAW DNI

Ambient

Temperature, C

DNI error

%

Corrected

DNI

300 0 -1.6% 305

600 0 -1.6% 610

950 0 -1.6% 965

300 20 0.0% 300

600 20 0.0% 600

950 20 0.0% 950

300 44 1.9% 294

600 44 1.9% 588

950 44 1.9% 932

SunCatcher

Energy

Production


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0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

: : : : : : : : : : : : : : : : : : : : :

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

: : : : : : : : : : : : : : : : : : : : :

Solar Resource Basics

Ragged DayClear Day

Global Horizontal Irradiation (W/m2)

PV

Diffuse Irradiation (W/m2)

PV

Direct Normal Irradiation(W/m2)

CSP / CPV

kWh/m2 paTwin tracking allowsplant to access full

irradiation in themorning and evening

Fixed horizontalwith limited

irradiation in non-noon hours Diffuse

irradiationincreaseswith cloud

coverage/ in lowerDNI areas

kWh/m2 pa

Tracking enables technologies to access more resource and higher capacity factorsCSP / CPV require direct radiation – PV operates with direct and diffuse radiation (global)

DNI (total kWh): 4.3

GHI (total kWh): 6.0Diffuse (total kWh): 2.7

DNI (total kWh): 10.2

GHI (total kWh): 7.7

Diffuse (total kWh): 0.7


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2. Output Quality

PV vs. CSP

• Traditional CSP plants have higher output quality due to thermal inertia and thermal storage

Even though SunCatcher units can stay on sun 5mins+ after DNI drops under threshold (300W/m2)actual power output is comparable to PV

Production on a Sunny Day*

Production on a Cloudy Day*

*Source: Black & Veatch

Power Output vs. DNI

Engine Ride-Trough Capability

Power (kW)

UnitsDNI (W/m2)

-200

0

200

400

600

800

1000

1200

1400

0

200

400

600

800

1000

1 6

: 1 5

1 6

: 1 7

1 6

: 1 9

1 6

: 2 1

1 6

: 2 3

1 6

: 2 5

1 6

: 2 7

1 6

: 2 9

1 6

: 3 1

1 6

: 3 3

1 6

: 3 5

1 6

: 3 7

1 6

: 3 9

1 6

: 4 1

1 6

: 4 3

1 6

: 4 5

1 6

: 4 7

1 6

: 4 9

1 6

: 5

1 DNI

kW

0

10

20

30

40

50

60

70

0

200

400

600

800

1000

1 6

: 1 5

1 6

: 1 7

1 6

: 1 9

1 6

: 2 1

1 6

: 2 3

1 6

: 2 5

1 6

: 2 7

1 6

: 2 9

1 6

: 3 1

1 6

: 3 3

1 6

: 3 5

1 6

: 3 7

1 6

: 3 9

1 6

: 4 1

1 6

: 4 3

1 6

: 4 5

1 6

: 4 7

1 6

: 4 9

1 6

: 5

1DNI

On Sun

DNI (W/m2)


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Thank You.The World Bank | 1818 H Street, NW | Washington DC, USA

www.esmap.com | [email protected]

SILVIA MARTINEZ ROMERO

[email protected]

20

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